Abstract
Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life (RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network (LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure. In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.
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Zhao-Hua Liu received the M. Sc. degree in computer science and engineering, and the Ph. D. degree in automatic control and electrical engineering from Hunan University, China in 2010 and 2012, respectively. He worked as a visiting researcher in Department of Automatic Control and Systems Engineering at University of Sheffield, UK from 2015 to 2016. He is currently an associate professor with School of Information and Electrical Engineering, Hunan University of Science and Technology, China. He has published a monograph in the field of biological immune system inspired hybrid intelligent algorithm and its applications, and published more than 30 research papers in refereed journals and conferences. He is a regular reviewer for several international journals and conferences.
His research interests include artificial intelligence and machine learning algorithm design, parameter estimation and control of permanent-magnet synchronous machine drives, and condition monitoring and fault diagnosis for electric power equipment.
Xu-Dong Meng received the B.Sc. degree in information and communications engineering from Hunan Institute of Technology, China in 2016, and the M. Sc. degree in automatic control and electrical engineering from Hunan University of Science and Technology, China in 2019.
His research interests include machine learning, data mining, and condition monitoring and fault diagnosis for electric power equipment.
Hua-Liang Wei received the Ph.D. degree in automatic control from University of Sheffield, UK in 2004. He is currently a senior lecturer with Department of Automatic Control and Systems Engineering, University of Sheffield, UK.
His research interests include evolutionary algorithms, identification and modelling for complex nonlinear systems, applications and developments of signal processing, system identification and data modelling to control engineering.
Liang Chen received the B. Eng. degree in automation from Henan University, China in 2018. He is currently a master student in automatic control and electrical engineering, Hunan University of Science and Technology, China.
His research interests include deep learning algorithm design and fault diagnosis of wind turbine transmission chains.
Bi-Liang Lu received the B.Eng. degree in electrical engineering and automation, the M. Sc. degree in automatic control and electrical engineering from Hunan University of Science and Technology, China in 2017 and 2020, respectively.
His research interests include deep learning algorithm design, and condition monitoring and fault diagnosis for electric power equipment.
Zhen-Heng Wang received the B. Sc. and M.Sc. degrees in automation from Beijing University of Chemical Technology, China in 2006 and 2009, respectively, and the Ph. D. degree in natural resource engineering from Laurentian University, Canada in 2014. Currently, he is a lecturer with Hunan University of Science and Technology, China.
His research interest includes process control, process fault diagnosis and artificial intelligence related subjects.
Lei Chen received the M. Sc. degree in computer science and engineering, and the Ph. D. degree in automatic control and electrical engineering from Hunan University, China in 2012 and 2017, respectively. He is currently a lecturer with School of Information and Electrical Engineering, Hunan University of Science and Technology, China.
His research interests include deep learning, network representation learning, information security of industrial control system and big data analysis.
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Liu, ZH., Meng, XD., Wei, HL. et al. A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings. Int. J. Autom. Comput. 18, 581–593 (2021). https://doi.org/10.1007/s11633-020-1276-6
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DOI: https://doi.org/10.1007/s11633-020-1276-6